Tag Archives: health

Life Expectancy Increase (12-14 Years) With 5 Factors

Following five lifestyle-related factors is associated with a gain in average life expectancy (Li et al. 2018). What are these factors? Not smoking, having a BMI between 18.5 to 24.9 kg/m2, engaging in more than 30 minutes of moderate to vigorous physical activity (at a minimum, walking ~3 miles per hour; 30 minutes of that = 1.5 miles of walking per day), moderate alcohol intake (5 to 15 g/d for women and 5 to 30 g/d for men), and a high diet quality score.

Starting at age 50y, having all 5 of these factors was associated with a life expectancy of an additional 43.1 years for women, and 37.6 years for men, which is an increase in average life expectancy of 14 years for women and 12 years for men, respectively:

Screen Shot 2019-09-29 at 12.49.55 PM.png

Quantifying whether or not you have the first 4 factors is easy, but what qualifies as having a high dietary score? The alternative healthy eating index (AHEI; McCullough et al. 2002) was used to define the dietary score. An AHEI score of more than 43.5 in women and 50 in men qualifies as having a high dietary quality. How is the AHEI defined?

If you eat more than 5 servings of vegetables (1 serving = ~3 ounces, or 80g) per day, you get 10 points. Similarly, more than 4 servings of fruit gets you 10 points. If you eat 1 serving (= 1.5 ounces, or 42 grams) of nuts and or soy protein (tofu) you get 10 points. If your intake of white meat (including fish, poultry) divided by red meat is greater than 4, you get 10 points. If you eat > 9 grams of cereal fiber (not 9 grams of grains, but the actual fiber content) per day, you get 10 points. For example, 9 grams of cereal fiber corresponds to 90g/day of dry oats. Alcohol is also included within the AHEI: if you have 1.5 – 2.5 servings of alcoholic drinks per day (for men) or 0.5 – 1.5 servings/day for women, that’s 10 points. Zero points would be not consuming alcoholic drinks, or > 3.5 drinks for men, and > 2.5 drinks per day for women. Having a polyunsaturated/saturated fat (P:S) intake > 0.5 yields 8 points, whereas a ratio > 0.7 yields 10 points. Consuming < 0.9 grams of trans fat per day yields 10 points, and finally, using a multivitamin for more than 5 years yields 10 points. To determine your score, have a a look at the median AHEI values reported for men:

Screen Shot 2019-09-29 at 10.11.15 AM

And for women:

Screen Shot 2019-09-29 at 10.13.37 AM.png

How many of the 5 factors do I have? I don’t smoke, my BMI is within the BMI range (my body weight was 158 this morning, so barely!), and I easily walk more than an hour/day + 3-4 days of exercise/week, so I qualify for the first 3 factors. However, I rarely drink alcohol, so I don’t qualify for that factor. What about the diet quality factor? To determine that, I’ll need to calculate if I have more than 50 AHEI points.

For the AHEI index, getting 5, 4, and 1 servings of veggies, fruit, and nuts per day is easy for me, so I’ve got 30 points so far. I eat oats once or twice/week, but not enough to get 9g of cereal fiber/day, so 0 points there. I eat 80 grams of sardines every day (560 grams/week), and ~150 grams of red meat per week, for a ratio of 3.7. That wouldn’t qualify me for 10 points, but 8 instead (see Quintile 4), where the white/red meat ratio would need to be higher than 2.5. I rarely drink alcohol, so 0 points for me there. Using last week’s dietary data, my P:S ratio is about 0.5, and my trans fat intake (almost exclusively from full-fat dairy) is 0.7 g/day, so I get 8 points and 10 points, respectively. In terms of multivitamin use, I only supplement with Vitamin D in the winter, and with a methylfolate-methylcobalamin-B6 stack (to reduce my homocysteine by ~10%). I haven’t been supplementing with that stack for more than five years, so I get a 0 there. Nonetheless, my score is 56 points, which would qualify me as having a high diet quality score.

Collectively, I have 4 of the 5 lifestyle factors that are associated with an increase in life expectancy. Based on the data from Li et al., my average life expectancy would be 85.4y. Adding in moderate alcohol intake would give me all 5 factors, and would result in a life expectancy gain of an additional 2.2 years. I’ve included 1-2 glasses of wine in my diet in the past, but it had no effect on my HDL or other circulating biomarkers, so I removed it. For me, the risk related to alcohol intake may not be worth the gain in life expectancy. Also note that these are average, population-based values, and I expect an additional gain in life expectancy gain because of my continuous quest for biological age optimization (https://michaellustgarten.wordpress.com/2019/09/09/quantifying-biological-age_!

References

Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH, Kaptoge S, Di Angelantonio E, Stampfer M, Willett WC, Hu FB. Impact of Healthy Lifestyle Factors on Life Expectancies in the US Population. Circulation. 2018 Jul 24;138(4):345-355. doi: 10.1161/CIRCULATIONAHA.117.032047.

McCullough ML, Feskanich D, Stampfer MJ, Giovannucci EL, Rimm EB, Hu FB, Spiegelman D, Hunter DJ, Colditz GA, Willett WC. Diet quality and major chronic disease risk in men and womenmoving toward improved dietary guidanceAm J Clin Nutr. 2002 Dec;76(6):1261-71.

If you’re interested, please have a look at my book!

Quantifying Biological Age

In an earlier post, I wrote about quantifying my biological age with aging.ai (https://michaellustgarten.wordpress.com/2018/06/26/maximizing-health-and-lifespan-is-calorie-restriction-essential/). The importance of that post is illustrated by the finding that based on data from 13 blood tests between 2016 – 2019, my average biological age is 29.2y, which is ~33% younger than my chronological age.

On my quest for optimal health, I’m striving to get as accurate as possible when it comes to quantifying biological age. While the aging.ai biomarker set is strongly correlated with biologic age (r = 0.80), in 2018 two papers were published (Liu et al., Levine et al.) that introduced “Phenotypic Age”, which includes a combination of 9 circulating biomarkers + chronological age that is better at predicting biological age (r = 0.94) than aging.ai. It includes analytes that are found on the standard blood chemistry screen, including albumin, creatinine, glucose, lymphocyte %, mean corpuscular volume (MCV), red blood cell distribution width (RDW), alkaline phosphatase, white blood cells, and an analyte that is not found on that panel, C-reactive protein (CRP). In addition, chronological age is included as a covariate.

So what’s my biological age based on the Phenotypic Age calculator? When I input my data from my latest blood test measurement on 6/4/2019, I get a biological age of 35.39y, which is 23% lower than my chronological age of 46. Not bad!

phenoage

To quantify your biological age with the Phenotypic Age calculator, input your data in the Excel file that is embedded within the first paragraph of the following link:

DNAmPhenoAge_gen

3.27.25 Edit: In the link above, note that the denominator in D17 should be 0.090165, not 0.09165. Additionally, the units for albumin should be g/dL (not mg/dL), and lymphocyte isn’t spelled correctly. I can’t upload a new link-I’d have to upgrade my WordPress account to be able to upload files (which is ridiculous!).

If you’re interested, please have a look at my book!

References

Liu Z, Kuo PL, Horvath S, Crimmins E, Ferrucci L, Levine M. A new aging measure captures morbidity and mortality riskacross diverse subpopulations from NHANES IV: A cohort studyPLoS Med. 2018 Dec 31;15(12):e1002718. doi: 10.1371/journal.pmed.1002718.

Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, Aviv A, Lohman K, Liu Y, Ferrucci L, Horvath S. An epigenetic biomarker of aging for lifespan and healthspanAging (Albany NY). 2018 Apr 18;10(4):573-591. doi: 10.18632/aging.101414.

LP(a), cardiovascular disease, and all-cause mortality: What’s optimal?

Very low, low, and high-density lipoproteins (VLDL, LDL, HDL, respectively) are commonly measured on the standard blood chemistry panel as measures of cardiovascular disease risk. Not included on that panel is another lipoprotein, Lp(a), which is a modified form of LDL. What’s the relationship between Lp(a) with disease risk?

A meta-analysis of 36 studies that included 126,634 subjects reported that Lp(a) > 30 mg/dL (65 nmol/L) was significantly associated with an increased risk for heart attacks, coronary heart disease-related deaths, and ischemic strokes (Erqou et al.  2009):

Screen Shot 2019-08-31 at 8.32.04 PM

Investigating further, of 2,100 candidate genes that were evaluated for predicting heart disease risk, genetic variation in the LPA gene was the strongest genetic risk factor (Clarke et al. 2009). Of the Lp(a)-related genes, SNPs for rs3798220 (increased risk allele = C) and rs10455872 (increased risk allele = G) were associated with a 92% and a 70% increased risk for coronary heart disease, respectively.

Based on these data, Lp(a) values less than 50 mg/dL (108 nmol/L) have been recommended, with 1-3 grams/day of niacin, which reduces Lp(a) levels, as the primary treatment for minimizing cardiovascular disease risk (Nordestgaard et al. 2010).

However, cardiovascular disease is only 1 outcome. What’s the data for Lp(a) and risk of death from all causes, not just cardiovascular disease-related deaths? In a study of 10,413 adults (average age, 55y), the lowest risk of death from all causes was reported for Lp(a) values of 270 mg/L (equivalent to 27 mg/dL, and 58 nmol/L). The log of 270 is 2.43, which corresponds to the lowest mortality risk on the chart below (Sawabe et al. 2012):

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Interestingly, all-cause mortality risk was significantly increased only for Lp(a) values < 80 mg/L (log 80 = 1.90; equivalent to 17 nmol/L), when compared with intermediate (80 – 550 mg/L; log values from 1.9 – 2.7 on the chart; equivalent to 17 – 118 nmol/L) and high Lp(a) (> 550 mg/L; log values > 2.7 on the chart; equivalent to > 118 nmol/L).

In addition to low Lp(a) values, an increased risk of death from all causes (and a shorter lifespan) have also been reported for high Lp(a). When compared with Lp(a) < 21 nmol/L, Lp(a) > 199 nmol/L was associated with a 20% increased all-cause mortality risk (Langsted et al. 2019). In addition, median lifespan was 1.4 years shorter for subjects that had  Lp(a) values > 199 nmol/L, when compared with < 21 nmol/L.

Based on the studies of Sawabe and Langsted, both low and high Lp(a) values may be bad for disease risk. What are my Lp(a) values?

I’ve been tracking Lp(a) for the past 14 years, first, approximately 1x/year until I was 40, and second, 9 times since 2015, when I started daily nutrition tracking. In addition, I’ve measured it 4x in 2019, with the goal of getting it closer to the 58 nmol/L value of the Sawabe study. When I first started measuring Lp(a) in 2005, it was ~150 nmol/L, which is way higher than the < 65 nmol/L that was reported for reduced cardiovascular disease risk in the Erqou meta-analysis, and the 58 nmol/L value that was reported for maximally reduced all-cause mortality risk in the Sawabe study:

Picture1

Fortunately, I was able to reduce my Lp(a) levels from those first values to levels closer to ~100 nmol/L, which is still too high. For the first 8 Lp(a) measurements, I didn’t track my nutrition, so I can’t say which factors helped me to reduce it. Also, note that I didn’t include the blood test measurement where I tried high dose niacin (3 g/day), which reduced my Lp(a) to 84 nmol/L, but also worsened my liver function,. My liver enzymes, AST and ALT doubled on high-dose niacin! What good is a reduced risk for cardiovascular disease if my risk for liver disease simultaneously goes up? Obviously, I quickly discontinued use of niacin to reduce Lp(a).

Also note the data on the chart since 2015, when I started daily nutritional tracking. Over that period, my average value over 9 Lp(a) measurements is 95.3 nmol/L. Although my average Lp(a) is still higher than it should be, it’s better than my pre-tracking Lp(a) average value of 115.6 nmol/L (p-value = 0.03 for the between-group comparison). In addition, on my last 3 measurements, my Lp(a) values were 75, 82, and 79 nmol/L. How have I been reducing it?

As I’ve mentioned in many blog posts, I’ve been weighing, logging, and tracking my nutrient intake since 2015. When I blood test, I can use the average dietary intake that corresponds to the blood test result, and with enough blood test results, I can look at correlations between my diet with blood test variables. Based on this approach, one possibility is my daily sodium intake. Shown below is a moderately strong correlation (r = 0.61, R^2 = 0.366) between my daily sodium intake with Lp(a). The higher my sodium intake, the lower my Lp(a) values.

lpa vs na.png

Can the strength of this approach be improved? Interestingly, I identified another moderately strong correlation (r = 0.69) between my lycopene intake with Lp(a): the higher my lycopene intake, the higher my Lp(a)! I then decided to include both sodium and lycopene in a linear regression model, and the correlation for both of these nutrients with Lp(a) is 0.90! So what will I do with this info?

The highest that my average dietary sodium intake has been in any blood testing period is ~2500 mg. Sodium levels higher than that seem to negatively affect my sleep, so I’m not interested in going higher than 2500 mg/day. Also, there may be a plateau effect for sodium, as values ~2500 mg/day didn’t associate with significantly lower Lp(a) values when compared with 2300 mg/day. I can, in contrast, reduce my lycopene intake, which comes almost exclusively from my daily watermelon intake. I usually eat ~7 oz/day, and for my next blood test I’ll reduce this to 5 oz/day. Based on the regression equation that includes sodium and lycopene, with a 2300 mg sodium intake and the amount of lycopene that corresponds to 5 oz. of daily watermelon (~6700 micrograms, down from ~9000 micrograms), I should expect to see a Lp(a) value ~67 nmol/L on my next blood test. If not, I’ll repeat this approach, looking for strong correlations between my diet with Lp(a), followed by tweaking my diet to obtain biomarker results that are close to optimal. Stay tuned my my next blood test data, coming in about 2 weeks!

If you’re interested, please have a look at my book!

 

References

Clarke, R., J. F. Peden, J. C. Hopewell, T. Kyriakou, A. Goel, S. C. Heath, S. Parish, S. Barlera, M. G. Franzosi, S. Rust, et al. 2009. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 361: 2518–2528.

Erqou, S., S. Kaptoge, P. L. Perry, A. E. Di, A. Thompson, I. R. White, S. M. Marcovina, R. Collins, S. G. Thompson, and J. Danesh. 2009. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA. 302: 412–423.

Langsted A, Kamstrup PR, Nordestgaard BG. High lipoprotein(a) and high risk of mortalityEur Heart J. 2019 Jan 4. [Epub ahead of print].

Sawabe M, Tanaka N, Mieno MN, Ishikawa S, Kayaba K, Nakahara K, Matsushita S; JMS Cohort Study Group. Low Lipoprotein(a) Concentration Is Associated with Cancer and All-Cause Deaths: A Population-Based Cohort Study (The JMS Cohort Study). PLoS One. 2012; 7(4): e31954. PLoS One. 2012;7(4):e31954.

Nordestgaard BG, Chapman MJ, Ray K, Borén J, Andreotti F, Watts GF, Ginsberg H, Amarenco P, Catapano A, Descamps OS, Fisher E, Kovanen PT, Kuivenhoven JA, Lesnik P, Masana L, Reiner Z, Taskinen MR, Tokgözoglu L, Tybjærg-Hansen A; European Atherosclerosis Society Consensus Panel. Lipoprotein(a) as a cardiovascular risk factor: current status. Eur Heart J. 2010 Dec;31(23):2844-53.

Tracking Deep Sleep-Can It Be Improved?

Deep sleep, the stage of sleep also known as “slow wave sleep” declines during aging. Based on a meta-analysis of 65 studies representing 3,577 subjects (aged 5 years to 102 years; Ohayon et al. 2004), slow wave sleep, expressed as a percentage of total sleep time decreases during aging from 25% in childhood to less than 10% in adults older than 65 years:Screen Shot 2019-02-16 at 5.14.10 PM.png Continue reading

100 Days Of Dietary Data

I’ve posted individual dietary days as an example of what and how much I eat (https://michaellustgarten.wordpress.com/2015/12/31/130-grams-of-fiber-2400-calories/). However, a few days of examples may not represent the whole dietary picture. To address this, below is my average nutrient intake for the past 100 days (from October 24, 2018-Feb 5 2019):

100 days of nutrition.png

Notice that my average values for many of these variables (i.e. potassium, selenium, Vitamin C, Vitamin K, etc.) are way above the RDA. For more info on that, I have several blog posts that explain the “why” behind that. Where am I getting those nutrients from? Shown below are 100-day averages for my food intake, ranked in order from most consumed (in grams, or ounces, if it’s a drink) to least:

100 days of foods.png

During the past 100 days, my top 5 foods in terms of daily intake include carrots, strawberries, red peppers, watermelon, and cauliflower. Scroll through the list to see how much I average on a daily basis for each food!

Please have a look at my book, if you’re interested!

Resting heart rate: What’s optimal?

One of the goals of my exercise program is to reduce my resting heart rate (RHR). A stronger heart beats less times per minute, but pumps more blood per beat. In contrast, a weaker heart beats more times per minute, but less blood per beat.

Is there an optimal level for RHR? Based on a meta-analysis of 59 studies that included 1,810,695 subjects, RHR values < 50 beats per minute (bpm) are associated with maximally reduced risk of death from all causes. Conversely, RHR values > 50 bpm are associated with a higher mortality risk (Aune et al. 2017):

Screen Shot 2019-02-02 at 10.48.29 AM

What’s my resting heart rate? Shown below is that data, tracked by WHOOP since August. Note that my RHR wasn’t significantly different from August until October, ranging from 51-53 bpm (average, 51.7). However, because I was tracking my RHR, I noticed that I was overtraining, leading to very high HRs, lower heart rate variability, and less deep sleep (topics for another post!) the day(s) after exercise. So early in November, I changed my exercise routine. As a result, from November until the end of January, my average RHR (49.7 bpm) has been significantly less (p-value =1E-10), and based on January’s average RHR, I’m trending closer to 47 bpm! Also note that * = significantly different when compared with August.

hr

What did I change in my exercise program? Since I’ve been in Boston (~9 years), I’ve walked 15-20 miles per week: it’s 1.1 miles to and from work, plus at least an hour of walking on Saturdays and Sundays. That’s a constant that hasn’t changed. In contrast, I split my 3-day weight training routine, which totaled ~5-6 hours/week into 3-5 days at less than an hour each session, and at a lower intensity with more reps. My strength is still as good as it was before, and as a result, my recovery HRs aren’t as high, thereby leading to a lower average RHR over time,. I’ve been training like that consistently for the past 30 years, but it took wearing a fitness tracker to change it!

 

Reference

Aune D, Sen A, ó’Hartaigh B, Janszky I, Romundstad PR, Tonstad S, Vatten LJ. Resting heart rate and the risk of cardiovascular diseasetotal cancer, and all-cause mortality – A systematic review and dose-response meta-analysis of prospective studiesNutr Metab Cardiovasc Dis. 2017 Jun;27(6):504-517.

 

If you’re interested in living longer and healthier, please have a look at my book!